Large-scale evaluation of automated detection and localization of bowhead whale calls in the Beaufort Sea.

2009 ◽  
Vol 126 (4) ◽  
pp. 2230
Author(s):  
Aaron M. Thode ◽  
Delphine Mathias ◽  
Christopher S. Nations ◽  
Trent L. McDonald ◽  
Michael Macrander
Keyword(s):  
Large Scale ◽  
Beaufort Sea ◽  
Automated Detection ◽  
Bowhead Whale ◽  
Scale Evaluation ◽  
Detection And Localization

scholarly journals Automated detection and localization of bowhead whale sounds in the presence of seismic airgun surveys

2012 ◽  
Vol 131 (5) ◽  
pp. 3726-3747 ◽  
Author(s):  
Aaron M. Thode ◽  
Katherine H. Kim ◽  
Susanna B. Blackwell ◽  
Charles R. Greene ◽  
Christopher S. Nations ◽  
...  
Keyword(s):  
Automated Detection ◽  
Bowhead Whale ◽  
Detection And Localization

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

Automated large scale evaluation of protein structure predictions

1999 ◽  
Vol 37 (S3) ◽  
pp. 7-14 ◽  
Author(s):  
Peter Lackner ◽  
Walter A. Koppensteiner ◽  
Francisco S. Domingues ◽  
Manfred J. Sippl
Keyword(s):  
Protein Structure ◽  
Large Scale ◽  
Scale Evaluation
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